function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel_silent(fcn,x0,H0,grad,crit,nit,varargin)
%[fhat,xhat,ghat,Hhat,itct,fcount,retcodehat] = csminwel(fcn,x0,H0,grad,crit,nit,varargin)
% fcn:   string naming the objective function to be minimized
% x0:    initial value of the parameter vector
% H0:    initial value for the inverse Hessian.  Must be positive definite.
% grad:  Either a string naming a function that calculates the gradient, or the null matrix.
%        If it's null, the program calculates a numerical gradient.  In this case fcn must
%        be written so that it can take a matrix argument and produce a row vector of values.
% crit:  Convergence criterion.  Iteration will cease when it proves impossible to improve the
%        function value by more than crit.
% nit:   Maximum number of iterations.
% varargin: A list of optional length of additional parameters that get handed off to fcn each
%        time it is called.
%        Note that if the program ends abnormally, it is possible to retrieve the current x,
%        f, and H from the files g1.mat and H.mat that are written at each iteration and at each
%        hessian update, respectively.  (When the routine hits certain kinds of difficulty, it
%        write g2.mat and g3.mat as well.  If all were written at about the same time, any of them
%        may be a decent starting point.  One can also start from the one with best function value.)
[nx,no]=size(x0);
nx=max(nx,no);
Verbose=1;
Ngrad= isempty(grad);
done=0;
itct=0;
fcount=0;
snit=100;

f0 = feval(fcn,x0,varargin{:});
if f0 > 1e50 
    return
end
if Ngrad
   if length(grad)==0
      [g badg] = feval(@numgrad,fcn,x0, varargin{:});
   else
      badg=any(find(grad==0));
      g=grad;
   end
else
   [g badg] = feval(grad,x0,varargin{:});
end
retcode3=101;
x=x0;
f=f0;
H=H0;
cliff=0;
while ~done
   g1=[]; g2=[]; g3=[];
   itct=itct+1;
   [f1 x1 fc retcode1] = csminit(fcn,x,f,g,badg,H,varargin{:});
   fcount = fcount+fc;
   if retcode1 ~= 1
      if retcode1==2 | retcode1==4
         wall1=1; badg1=1;
      else
         if Ngrad
            [g1 badg1] = feval(@numgrad,fcn, x1,varargin{:});
         else
            [g1 badg1] = feval(grad,x1,varargin{:});
         end
         wall1=badg1;
      end
      if wall1 % & (~done) by Jinill
         Hcliff=H+diag(diag(H).*rand(nx,1));
         [f2 x2 fc retcode2] = feval(@csminit,fcn,x,f,g,badg,Hcliff,varargin{:});
         fcount = fcount+fc; % put by Jinill
         if  f2 < f
            if retcode2==2 | retcode2==4
                  wall2=1; badg2=1;
            else
               if Ngrad
                  [g2 badg2] = feval(@numgrad,fcn, x2,varargin{:});
               else
                  [g2 badg2] = feval(grad,x2,varargin{:});
               end
               wall2=badg2;
               %badg2
            end
            if wall2
               %disp('Cliff again.  Try traversing')
               if norm(x2-x1) < 1e-13
                  f3=f; x3=x; badg3=1;retcode3=101;
               else
                  gcliff=((f2-f1)/((norm(x2-x1))^2))*(x2-x1);
                  if(size(x0,2)>1)
                      gcliff=gcliff'; 
                  end
                  [f3 x3 fc retcode3] = feval(@csminit,fcn,x,f,gcliff,0,eye(nx),varargin{:});
                  fcount = fcount+fc; % put by Jinill
                  if retcode3==2 | retcode3==4
                     wall3=1; badg3=1;
                  else
                     if Ngrad
                        [g3 badg3] = feval(@numgrad,fcn, x3,varargin{:});
                     else
                        [g3 badg3] = feval(grad,x3,varargin{:});
                     end
                     wall3=badg3;
                     %badg3
                     %save g3 g3 x3 f3 varargin;
                  end
               end
            else
               f3=f; x3=x; badg3=1; retcode3=101;
            end
         else
            f3=f; x3=x; badg3=1;retcode3=101;
         end
      else
         % normal iteration, no walls, or else we're finished here.
         f2=f; f3=f; badg2=1; badg3=1; retcode2=101; retcode3=101;
      end
   else 
      f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
   end
   %how to pick gh and xh
   if f3 < f - crit & badg3==0
      ih=3; 
      fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
   elseif f2 < f - crit & badg2==0
      ih=2; 
      fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
   elseif f1 < f - crit & badg1==0
      ih=1; 
      fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
   else
      [fh,ih] = min([f1,f2,f3]);
      switch ih
         case 1
            xh=x1;
         case 2
            xh=x2;
         case 3
            xh=x3;
      end %case
      retcodei=[retcode1,retcode2,retcode3];
      retcodeh=retcodei(ih);
      if exist('gh')
         nogh=isempty(gh);
      else
         nogh=1;
      end
      if nogh
         if Ngrad
            [gh badgh] = feval(@numgrad,fcn,xh,varargin{:});
         else
            [gh badgh] = feval(grad, xh,varargin{:});
         end
      end
      badgh=1;
   end
   stuck = (abs(fh-f) < crit);
   if (~badg)&(~badgh)&(~stuck)
      H = feval(@bfgsi,H,gh-g,xh-x);
   end
   if Verbose
      %disp(sprintf('Improvement on iteration %d = %18.9f',itct,f-fh))
   end
      if itct > nit
         %disp('iteration count termination')
         done = 1;
      elseif stuck
         %disp('improvement < crit termination')
         done = 1;
      end
      rc=retcodeh;
      if rc == 1
         %disp('zero gradient')
      elseif rc == 6
         %disp('smallest step still improving too slow, reversed gradient')
      elseif rc == 5
         %disp('largest step still improving too fast')
      elseif (rc == 4) | (rc==2)
         %disp('back and forth on step length never finished')
      elseif rc == 3
         %disp('smallest step still improving too slow')
      elseif rc == 7
         %disp('warning: possible inaccuracy in H matrix')
      end
   % end
   f=fh;
   x=xh;
   g=gh;
   badg=badgh;
end
